A new rolling bearing fault diagnosis method based on multiscale permutation entropy and improved support vector machine based binary tree

Yongbo Li, Minqiang Xu, Yu Wei, Wenhu Huang

Research output: Contribution to journalArticlepeer-review

270 Scopus citations

Abstract

A new bearing vibration feature extraction method based on multiscale permutation entropy (MPE) and improved support vector machine based binary tree (ISVM-BT) is put forward in this paper. Local mean decomposition (LMD), a new self-adaptive time-frequency analysis method, is utilized to decompose the roller bearing vibration signal into a set of product functions (PFs) and then MPE method is used to characterize the complexity of the principal PF component in different scales. After the feature extraction, a new pattern recognition approach called ISVM-BT is introduced to accomplish the fault identification automatically, which has the priority of high recognition accuracy compared with other classifiers. Besides, the Laplacian score (LS) is introduced to refine the fault feature by sorting the scale factors. Finally, the rolling bearing fault diagnosis method based on LMD, MPE, LS and ISVM-BT is proposed and the experimental results indicate the proposed method is effective in identifying the different categories of rolling bearings.

Original languageEnglish
Pages (from-to)80-94
Number of pages15
JournalMeasurement: Journal of the International Measurement Confederation
Volume77
DOIs
StatePublished - 1 Jan 2016
Externally publishedYes

Keywords

  • Fault diagnosis
  • Improved support vector machine based binary tree (ISVM-BT)
  • Laplacian score (LS)
  • Local mean decomposition (LMD)
  • Multi-scale permutation entropy (MPE)

Fingerprint

Dive into the research topics of 'A new rolling bearing fault diagnosis method based on multiscale permutation entropy and improved support vector machine based binary tree'. Together they form a unique fingerprint.

Cite this